Imagen de Portada

VFOG: Variance-Reduced Fast Optimistic Gradient Methods for a Class of Nonmonotone Generalized Equations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Tran-Dinh, Quoc, Nguyen-Trung, Nghia
Formato:	Preprint
Publicado:	2025
Materias:	Optimization and Control Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions
por: Tran-Dinh, Quoc, et al.
Publicado: (2026)

Variance-Reduced Fast Operator Splitting Methods for Generalized Equations
por: Tran-Dinh, Quoc
Publicado: (2025)

A Class of Accelerated Fixed-Point-Based Methods with Delayed Inexact Oracles and Its Applications
por: Nguyen-Trung, Nghia, et al.
Publicado: (2025)

Stochastic Variance-Reduced Forward-Reflected-Backward Splitting Methods for Nonmonotone Generalized Equations
por: Tran-Dinh, Quoc
Publicado: (2024)

Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems
por: Tran-Dinh, Quoc, et al.
Publicado: (2023)

Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates
por: Tran-Dinh, Quoc, et al.
Publicado: (2024)

Accelerated Extragradient-Type Methods -- Part 2: Generalization and Sublinear Convergence Rates under Co-Hypomonotonicity
por: Tran-Dinh, Quoc, et al.
Publicado: (2025)

Variance-Reduced Fast Krasnoselkii-Mann Methods for Finite-Sum Root-Finding Problems
por: Tran-Dinh, Quoc
Publicado: (2024)

Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
por: Tran-Dinh, Quoc, et al.
Publicado: (2024)

Shuffling Momentum Gradient Algorithm for Convex Optimization
por: Tran, Trang H., et al.
Publicado: (2024)

Omega: Optimistic EMA Gradients
por: Ramirez, Juan, et al.
Publicado: (2023)

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
por: Liang, Ling, et al.
Publicado: (2024)

Gap Safe Screening Rules for Fast Training of Robust Support Vector Machines under Feature Noise
por: Nguyen, Tan-Hau, et al.
Publicado: (2026)

Variance-Reduced $(\varepsilon,δ)-$Unlearning using Forget Set Gradients
por: Van Waerebeke, Martin, et al.
Publicado: (2026)

The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
por: Daskalakis, Constantinos, et al.
Publicado: (2018)

Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
por: Upadhyay, Antesh, et al.
Publicado: (2026)

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
por: Wang, Lei, et al.
Publicado: (2022)

Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
por: Chayti, El Mahdi, et al.
Publicado: (2023)

Breaking the Stochasticity Barrier: An Adaptive Variance-Reduced Method for Variational Inequalities
por: Jeong, Yungi, et al.
Publicado: (2026)

A Steepest Gradient Method with Nonmonotone Adaptive Step-sizes for the Nonconvex Minimax and Multi-Objective Optimization Problems
por: Anh, Nguyen Duc, et al.
Publicado: (2025)

Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
por: Jiang, Ruichen, et al.
Publicado: (2024)

Dual Optimistic Ascent (PI Control) is the Augmented Lagrangian Method in Disguise
por: Ramirez, Juan, et al.
Publicado: (2025)

Layer-wise Quantization for Quantized Optimistic Dual Averaging
por: Nguyen, Anh Duc, et al.
Publicado: (2025)

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
por: Medyakov, Daniil, et al.
Publicado: (2025)

Stochastic Gradient Langevin Dynamics with Variance Reduction
por: Huang, Zhishen, et al.
Publicado: (2021)

Divergence Results and Convergence of a Variance Reduced Version of ADAM
por: Wang, Ruiqi, et al.
Publicado: (2022)

On Stability in Optimistic Bilevel Optimization
por: Royset, Johannes O.
Publicado: (2024)

On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error
por: Nguyen, Quang Minh, et al.
Publicado: (2022)

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
por: Nguyen, Duc Toan, et al.
Publicado: (2025)

Adaptive Optimization via Momentum on Variance-Normalized Gradients
por: Patitucci, Francisco, et al.
Publicado: (2026)

Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates
por: Ji, Yao, et al.
Publicado: (2026)

Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
por: Keane, Ronan
Publicado: (2024)

Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
por: Boveiri, Mohammad, et al.
Publicado: (2024)

Tail Distribution of Regret in Optimistic Reinforcement Learning
por: Khodadadian, Sajad, et al.
Publicado: (2025)

Optimistic Online LQR via Intrinsic Rewards
por: Bartos, Marcell, et al.
Publicado: (2026)

Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning
por: Zeng, Sihan, et al.
Publicado: (2024)

Optimistic Online-to-Batch Conversions for Accelerated Convergence and Universality
por: Yan, Yu-Hu, et al.
Publicado: (2025)

Optimistic Online Non-stochastic Control via FTRL
por: Mhaisen, Naram, et al.
Publicado: (2024)

An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints
por: Lekeufack, Jordan, et al.
Publicado: (2024)

Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
por: Dereziński, Michał
Publicado: (2022)